silva_snarf Reproduction Dossier

articulated implicit shape with forward-skinning root search. This dossier connects the source mechanism to its SILVA implementation, compact evidence, replaceable components, and source-scale route. Existing tests and notebooks remain the executable authority.

Evidence boundary

The mechanism is compact-verified in the package suite. The final source-scale stage remains planned until the cited data, complete optimization budget, checkpoints, and evaluation protocol have actually run.

Identity and Sources

Field	Value
Domain	geometry and distributions
Task contract	posed query points and bone transforms -> canonical roots and occupancy
Source relation	paper-adaptation
References	[62]
Repositories	https://github.com/xuchen-ethz/snarf
Editable scale plan	experiments/reproduction/configs/silva_snarf.json

Governing Equation

The domain-level state contract is

\[ \mu^\star=\Phi_\theta(\mu^\star;\nu),\qquad \widehat y=Q_\psi(\mu^\star). \]

The implementation registry specializes it operationally as

d_w(x,B)=sum_b w_b(x) B_b x; d_w(x_star,B)-x_posed=0

Define the root residual

\[ R_\theta(z;x)=z-T_\theta(z;x). \]

At a regular equilibrium, differentiating \(R_\theta(z^\star;x)=0\) gives

\[ \frac{\partial z^\star}{\partial x} = \left(I-\frac{\partial T_\theta}{\partial z}\right)^{-1} \frac{\partial T_\theta}{\partial x}. \]

This identity explains why the forward residual, the conditioning derivative, and the adjoint linear solve must be diagnosed separately from the task metric.

What Is Preserved

• pose-independent canonical blend-weight field and pose-conditioned occupancy field
• linear blend forward deformation with inverse-bone multi-start root initialization
• implicit canonical correspondences, residual filtering, and soft occupancy union

What Can Be Replaced

Each item below is an explicit control rather than an undocumented modification:

• replace canonical weight and occupancy fields
• supply alternate skinning transforms, root maps, or correspondence aggregation
• query points
• bone starts
• root history
• occupancy-grid resolution

Constructor and Shape Contract

silva_snarf(coordinate_dim: 'int' = 3, bones: 'int' = 24, *, pose_dim: 'int' = 0, hidden_dim: 'int' = 64, weight_field: 'nn.Module | None' = None, occupancy_field: 'nn.Module | None' = None, root_step: 'float' = 1.0, correspondence_tol: 'float' = 0.0001, aggregation_temperature: 'float' = 20.0, config: 'SolverConfig | None' = None)

The transition must preserve the declared equilibrium-state shape even when the encoder, branch operators, constraints, solver, and readout are replaced. Test the transition by itself before testing the complete root solve.

Progressive Experiment Ladder

1. Equation and tensor contract

Objective: Make the state, conditioning variables, operator, and readout explicit.

Procedure:

• Write and evaluate the family equation: d_w(x,B)=sum_b w_b(x) B_b x; d_w(x_star,B)-x_posed=0
• Declare every tensor axis, boundary, mask, graph, or physical unit.
• Check the transition output has exactly the same shape as the equilibrium state.

Acceptance checks:

• finite transition values
• shape-preserving state update
• all conditioning variables affect the intended branch

Evidence target: contract-verified.

2. Primitive mechanism reconstruction

Objective: Build the retained source mechanism from replaceable modules.

Procedure:

• pose-independent canonical blend-weight field and pose-conditioned occupancy field
• linear blend forward deformation with inverse-bone multi-start root initialization
• implicit canonical correspondences, residual filtering, and soft occupancy union

Acceptance checks:

• primitive modules expose trainable parameters and gradients
• mechanism-specific invariance or constraint check passes
• direct transition evaluation is deterministic under a fixed seed

Evidence target: compact-verified.

3. Public abstraction equivalence

Objective: Verify that the assembled family evaluates the same transition as its primitives.

Procedure:

• Copy the primitive module parameters into the public family constructor.
• Evaluate one transition and one complete equilibrium with identical inputs.
• Compare outputs, residuals, and parameter gradients with declared tolerances.

Acceptance checks:

• transition outputs agree
• equilibrium residual is finite and decreases
• primitive and assembled gradients agree on the compact case

Evidence target: compact-verified.

4. Compact real or analytic task

Objective: Exercise training, evaluation, diagnostics, and serialization end to end.

Procedure:

• make_snarf_stick_dataset gives licensed-data-free articulated transforms, canonical points, and posed queries.

Acceptance checks:

• record intersection over union
• record correspondence residual/success
• record unseen-pose reconstruction
• record root evaluations
• checkpoint reload reproduces the recorded prediction
• result record contains data and configuration fingerprints

Evidence target: compact-verified.

5. Official-data subset

Objective: Validate the complete source data path before spending the full budget.

Procedure:

• Acquire the permitted SMPL and motion/mesh assets and run the source point-sampling preprocessing for a declared subject split.
• Freeze preprocessing, split logic, metric code, and checkpoint format.
• Run a deterministic subset large enough to expose batching and memory failures.

Acceptance checks:

• dataset receipt and checksum are stored
• resume and evaluation paths reproduce the same subset metric
• memory and runtime are measured rather than estimated

Evidence target: subset-verified.

6. Source-scale reproduction or declared extension

Objective: Run the cited protocol, or change it explicitly as a SILVA extension.

Procedure:

• Acquire the permitted SMPL and motion/mesh assets and run the source point-sampling preprocessing for a declared subject split.
• Train canonical blend weights and occupancy with inverse-bone starts, Broyden roots, residual filtering, and pose conditioning.
• Evaluate within-distribution and unseen poses, correspondence success, occupancy quality, and marching-cubes reconstruction with fixed settings.
• source subject meshes, bone transforms, canonical pose, query sampler, and train/validation sequences
• 2D Stick or DFaust/AMASS/CAPE access, occupancy labels, bootstrap losses, and root threshold
• unseen-pose reconstruction metrics, correspondence success, and mesh extraction settings

Acceptance checks:

• all required artifacts are archived
• reported metrics use the cited evaluation protocol
• every architectural or training deviation is listed
• claims match the achieved evidence status

Evidence target: planned.

Data, Access, and Storage

Candidate datasets:

• 2D Stick
• DFaust/AMASS
• CAPE

Authoritative routes:

• https://github.com/xuchen-ethz/snarf
• https://amass.is.tue.mpg.de/
• https://dfaust.is.tue.mpg.de/
• https://cape.is.tue.mpg.de/
• https://smpl.is.tue.mpg.de/

Access obligations:

• The implementation and test assets are public, while SMPL, AMASS, D-FAUST, and CAPE require their own registrations or licenses.
• Keep raw licenses outside package artifacts and record the exact subject, sequence, clothing, and preprocessing revision.

Storage planning:

• Budget raw meshes and motion separately from sampled occupancy/query tensors.
• Query-cache bytes scale with frames * samples per frame * (coordinates + occupancy + optional skinning labels) * bytes per value.

Preprocessing record:

• record dataset version, split, normalization, shape convention, and seed
• preserve masks, graph indices, boundaries, or physical units required by the domain

Metrics and Current Evidence

Required metrics:

• intersection over union
• correspondence residual/success
• unseen-pose reconstruction
• root evaluations

This family is verified through its listed mechanism tests and executed notebook. It is not included in a same-task comparison when another family does not share its input, state, output, and loss contract. The absence of a comparison row is therefore a scope decision, not missing implementation evidence.

Executed notebook paths:

• notebooks/package_api/31_silva_snarf_forward_skinning.ipynb

Mechanism tests:

• tests/test_emerging_equilibria.py

Compact Defaults

Option	Value
tier	'smoke'
config	SolverConfig(solver='broyden', max_iter=12, tol=1e-05, alpha=1.0, history=3, ridge=0.0001, beta=1.0, stop_mode='relative', relative_eps=1e-08, anderson_batch_dims=1, track_residuals=True, reengage=True, backward_mode='implicit', backward_solver='gmres', backward_max_iter=20, backward_tol=1e-05, backward_stop_mode='relative', backward_relative_eps=1e-08, phantom_steps=1, phantom_tau=1.0, neumann_terms=5, shine_refine_steps=0, indexing=(), return_best=True)

Full Defaults

Option	Value
tier	'full'
config	SolverConfig(solver='broyden', max_iter=60, tol=1e-05, alpha=1.0, history=6, ridge=0.0001, beta=1.0, stop_mode='relative', relative_eps=1e-08, anderson_batch_dims=1, track_residuals=True, reengage=True, backward_mode='implicit', backward_solver='gmres', backward_max_iter=80, backward_tol=1e-05, backward_stop_mode='relative', backward_relative_eps=1e-08, phantom_steps=1, phantom_tau=1.0, neumann_terms=5, shine_refine_steps=0, indexing=(), return_best=True)

Defaults establish a starting budget; the cited source protocol takes precedence whenever reproduction is the claim.

Source-Scale Checklist

• Acquire the permitted SMPL and motion/mesh assets and run the source point-sampling preprocessing for a declared subject split.
• Train canonical blend weights and occupancy with inverse-bone starts, Broyden roots, residual filtering, and pose conditioning.
• Evaluate within-distribution and unseen poses, correspondence success, occupancy quality, and marching-cubes reconstruction with fixed settings.

Benchmark-specific requirements:

• source subject meshes, bone transforms, canonical pose, query sampler, and train/validation sequences
• 2D Stick or DFaust/AMASS/CAPE access, occupancy labels, bootstrap losses, and root threshold
• unseen-pose reconstruction metrics, correspondence success, and mesh extraction settings

Required archived artifacts:

• machine-readable model and solver configuration
• dataset receipt with source revision, split, license, and checksum
• preprocessing and normalization record
• seeded training and evaluation log
• checkpoint and optimizer-resume state for trained experiments
• task metrics and equilibrium diagnostics in a machine-readable result
• runtime, peak-memory, device, precision, and dependency record
• declared deviations from the cited protocol

Reporting Rule

Report the achieved evidence status, not the intended one. A compact or subset run may validate the implementation and data path, but only a completed cited protocol supports a source-scale reproduction statement. Modified operators are valuable SILVA extensions when every deviation is named and measured.

Where to Go Next

Question	Page
Where are all family dossiers?	Family Dossier Index
How is a custom family assembled?	Advanced Extension Handbook
How are experiment stages represented in the API?	Research-Depth API
Which lab inspects every dossier?	Family Dossier Lab